Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions

The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to det...

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Bibliographic Details
Published in:Multimodal technologies and interaction 2022-03, Vol.6 (3), p.18
Main Authors: Pathi, Sai Krishna, Kiselev, Andrey, Loutfi, Amy
Format: Article
Language:English
Subjects:
Art galleries & museums
F-formations
Formations
group interactions
Human performance
human-robot interaction
Kendon formations
Robots
Sensors
Service robots
Simulation
Social interaction
social robotics
Teams
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Summary:The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.
ISSN:2414-4088
2414-4088
DOI:10.3390/mti6030018